Python License Plate Detection4/23/2021
Process is: license plate location, license plate image binarization, character normalized plate, license plate characters Refinement.After the pa. targetblank This procedure is the license plate recognition matlab source code.
After the partition plate s for 16 8 the size of six images into the neural network identification, the samples because of too many here do not give readers their extraction. Although no specific license plate number recognition, but the image processing type or very good use. Edge plate Is commonly used in license plate detection as a detectionTechnique.nbsp preprocessing;Key Tec. Python Plate Detection License Plate RecognitionEmail: quangnhatnguyenlegmail.com Follow 74 1 74 74 1 Machine Learning Computer Vision Artificial Intelligence Python License Plate Recognition More from Quang Nguyen Follow I am a researcher in the field of Robotics, Computer Vision and Artificial Intelligence. Nevertheless, a L i cense Plate image at this stage does not have much meaning to our digital system except a distributed amplitude of color. In this article, I will show you step-by-step how can we segmented key characters from License Plate using Python and OpenCV. ![]() Tools and Libraries: Python 3.7 Jupyter Notebook Numpy 1.17.4 Matplotlib 3.2.1 OpenCV 4.1.0 Github repository Notebook of part 2 Image processing To begin with, we need to applies several processing techniques to reduce noise and emphasize key features of license characters. We will use an image of license plate extracted from Platesexamplegermanycarplate.jpg (Fig. Python Plate Detection How To Extract PlateIf you do not know how to extract plate license from car images, you can refer Part 1 of this article here. Figure 2: Obtained license plate image from Part 1 The image processing which we shall implement on our image is: Convert to 255 scale. Extracted license image from Wpod-Net is interpreted as 01 scale, thus we need to convert it to 8-bit scale as standard image. Color plays an negligible role to understand the license plate, thus we can remove it to optimize computational power. Blur image. Blur technique is performed to remove noise and irrelevant information. In the example code, I used Gaussian Blur with kernel size of (7,7) but this value can be tuned depending on your image. The higher of the kernel size, the less noise but more information is lost. Image thresholding. We set a threshold so that any smaller pixel value than it would be converted to 255 and vice versa. This type of thresholding is called inverse binary thresholding. In the example code, I used threshold value of 180, this value can be modified to be more compatible with your image. Dilation. This is a technique to increase the white region of the image. By implementing dilation, we want to enhance the white contour of each character, Code for this part is demonstrated as below: Figure 3: Image Processing Determine contour of License characters Next, we will use findContours function of OpenCV to identify the coordinates of license character. This function is based on a simple theory: contours is simply curve joining all continuous point (along the boundary) sharing the same color and intensity. I created a function called sortcontours which basically sorts founded contours from left to right. This is essential since we want not only to recognize the character but also arrange them in correct order. As can be seen in the example code as below, the ratio in line 16 is equal to the height divided by the width of the contour. Since we know that our license character usually have greater height than width, we can filter the irrelevant contours by select only contours with ratio between 1 to 3.5 ( line 18 ). We also know that each license character should have its height greater than half of plate height, thus we can add another contour filter as line 18. Later, we draw bounding with all contours that passes these filters ( line 20 ), applies binary thresholding on each determined contours ( line 25 ) and append them to list cropcharacters. Detect 7 letters. Figure 4: Define License character contours Visualize our segmented characters Now, we already have all segmented characters storing in cropcharacters. We can visualize the with matplotlib using the example code as below. In Part 3, we will train Neural Network model which is able to recognize and convert those characters to digital letters. Figure 5: Segmented characters Link to the whole series: Part 1: Detection License Plate with Wpod-Net Part 2: Plate character segmentation with OpenCV Part 3: Recognize plate license characters with OpenCV and Deep Learning Written by Quang Nguyen I am a researcher in the field of Robotics, Computer Vision and Artificial Intelligence.
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